Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. ...

Markovian models with hidden state are widely-used formalisms for modeling sequential phenomena. Learnability of these models has been well studied when the sample is given in batch mode, and algorithms with PAC-like ...

In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to
automata whose transitions may take varying time lengths, governed by exponential distributions.

Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We ...

We present a method for transforming an Equivalence-query algorithm using Q queries into a PAC-algorithm using Q/epsilon + O( (Q^(2/3) / epsilon ) * log(Q / delta) examples in expectation. The method is a variation of that ...

We study a distribution dependent form of PAC learning
that uses probability distributions related to Kolmogorov complexity.
We relate the PACS model, defined by Denis, D'Halluin and Gilleron,
with the standard simple-PAC ...

We prove that log n-decision lists - the class of decision lists such that all their terms have low Kolmogorov complexity - are learnable in the simple PAC learning model. The proof is based on a transformation from an ...